floyd-steinberg dithering samples

dave.jpg

dave_2.jpg

diffuse.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import cv2\n",
    "\n",
    "def getDimensions(copy):\n",
    "    return len(copy), len(copy[0])\n",
    "\n",
    "def find_closest_palette_color(y,x,copy):\n",
    "    return 255 if copy[y][x] > 127 else 0\n",
    "\n",
    "def add(y,x,color,copy):\n",
    "    ly, lx = getDimensions(copy)\n",
    "    if x >= 0 and x < lx and y >= 0 and y < ly:\n",
    "        copy[y][x] = max(0, min(copy[y][x] + int(color), 255))\n",
    "        \n",
    "def show(image, name):\n",
    "    plt.imshow(image)\n",
    "    plt.title(name)\n",
    "    plt.show()\n",
    "\n",
    "def writeImage(image, name):\n",
    "    cv2.imwrite(name, image);\n",
    "\n",
    "def loadAndResize(name):\n",
    "    img = cv2.cvtColor(cv2.imread(name), cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "    height, width = img.shape\n",
    "    scale = min(1, 9999 / max(height, width))\n",
    "\n",
    "    return cv2.resize(img, (int(width * scale), int(height * scale)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dither(copy):\n",
    "    ly, lx = getDimensions(copy)\n",
    "\n",
    "    for y in range(0, ly):\n",
    "        for x in range(0, lx):\n",
    "\n",
    "            oldpixel = copy[y][x]\n",
    "            newpixel = find_closest_palette_color(y, x, copy)\n",
    "            copy[y][x] = newpixel\n",
    "            quant_error = oldpixel - newpixel\n",
    "            add(y, x+1, quant_error * 7 / 16, copy);\n",
    "            add(y+1, x+1, quant_error * 1 / 16, copy);\n",
    "            add(y+1, x, quant_error * 5 / 16, copy);\n",
    "            add(y+1, x-1, quant_error * 3 / 16, copy);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process(path, extension):\n",
    "    \n",
    "    img = loadAndResize(path + extension)\n",
    "\n",
    "    show(img, path)\n",
    "    dither(img)\n",
    "    show(img, path + \" (DITHERED)\")\n",
    "    \n",
    "    writeImage(img, path + \"_2\" + extension)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "list = [[\"/Users/onatbas/Desktop/diffuse/IMG_0137\", \".JPG\"],\n",
    "        [\"/Users/onatbas/Desktop/diffuse/dave\", \".jpg\"],\n",
    "        [\"/Users/onatbas/Desktop/diffuse/g\", \".bmp\"],\n",
    "        [\"/Users/onatbas/Desktop/diffuse/ikili\", \".jpg\"],\n",
    "        [\"/Users/onatbas/Desktop/diffuse/prof\", \".jpg\"],\n",
    "        [\"/Users/onatbas/Desktop/diffuse/IMG_7379\", \".JPG\"],\n",
    "        [\"/Users/onatbas/Desktop/diffuse/gard\", \".jpeg\"],\n",
    "        [\"/Users/onatbas/Desktop/diffuse/onat\", \".jpg\"]]\n",
    "\n",
    "for name in list:\n",
    "    process(name[0], name[1]);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
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}

g.bmp

g_2.bmp

gard.jpeg

IMG_0137.JPG

IMG_0137_2.JPG

IMG_7379.JPG

IMG_7379_2.JPG

gard_2.jpeg

ikili.jpg

ikili_2.jpg

metallic_dave.jpg

onat.jpg

onat_2.jpg

prof.jpg

prof_2.jpg